batteries-included

Batteries Included is an all-in-one platform for building and running modern applications, simplifying cloud infrastructure complexity. It offers production-ready capabilities through an intuitive interface, focusing on automation, security, and enterprise-grade features. The platform includes databases like PostgreSQL and Redis, AI/ML capabilities with Jupyter notebooks, web services deployment, security features like SSL/TLS management, and monitoring tools like Grafana dashboards. Batteries Included is designed to streamline infrastructure setup and management, allowing users to concentrate on application development without dealing with complex configurations.

AppFlowy Cloud is a secure user authentication, file storage, and real-time WebSocket communication tool written in Rust. It is part of the AppFlowy ecosystem, providing an efficient and collaborative user experience. The tool offers deployment guides, development setup with Rust and Docker, debugging tips for components like PostgreSQL, Redis, Minio, and Portainer, and guidelines for contributing to the project.

Middleware is an open-source engineering management tool that helps engineering leaders measure and analyze team effectiveness using DORA metrics. It integrates with CI/CD tools, automates DORA metric collection and analysis, visualizes key performance indicators, provides customizable reports and dashboards, and integrates with project management platforms. Users can set up Middleware using Docker or manually, generate encryption keys, set up backend and web servers, and access the application to view DORA metrics. The tool calculates DORA metrics using GitHub data, including Deployment Frequency, Lead Time for Changes, Mean Time to Restore, and Change Failure Rate. Middleware aims to provide DORA metrics to users based on their Git data, simplifying the process of tracking software delivery performance and operational efficiency.

Coral Cloud Resorts is a sample hospitality application that showcases Data Cloud, Agents, and Prompts. It provides highly personalized guest experiences through smart automation, content generation, and summarization. The app requires licenses for Data Cloud, Agents, Prompt Builder, and Einstein for Sales. Users can activate features, deploy metadata, assign permission sets, import sample data, and troubleshoot common issues. Additionally, the repository offers integration with modern web development tools like Prettier, ESLint, and pre-commit hooks for code formatting and linting.

Build your dream team with Autogen is a repository that leverages Microsoft Autogen 0.4, Azure OpenAI, and Streamlit to create an end-to-end multi-agent application. It provides an advanced multi-agent framework based on Magentic One, with features such as a friendly UI, single-line deployment, secure code execution, managed identities, and observability & debugging tools. Users can deploy Azure resources and the app with simple commands, work locally with virtual environments, install dependencies, update configurations, and run the application. The repository also offers resources for learning more about building applications with Autogen.

XLang™ is a cutting-edge language designed for AI and IoT applications, offering exceptional dynamic and high-performance capabilities. It excels in distributed computing and seamless integration with popular languages like C++, Python, and JavaScript. Notably efficient, running 3 to 5 times faster than Python in AI and deep learning contexts. Features optimized tensor computing architecture for constructing neural networks through tensor expressions. Automates tensor data flow graph generation and compilation for specific targets, enhancing GPU performance by 6 to 10 times in CUDA environments.

Skynet is an API server for AI services that wraps several apps and models. It consists of specialized modules that can be enabled or disabled as needed. Users can utilize Skynet for tasks such as summaries and action items with vllm or Ollama, live transcriptions with Faster Whisper via websockets, and RAG Assistant. The tool requires Poetry and Redis for operation. Skynet provides a quickstart guide for both Summaries/Assistant and Live Transcriptions, along with instructions for testing docker changes and running demos. Detailed documentation on configuration, running, building, and monitoring Skynet is available in the docs. Developers can contribute to Skynet by installing the pre-commit hook for linting. Skynet is distributed under the Apache 2.0 License.

OramaCore is a database designed for AI projects, answer engines, copilots, and search functionalities. It offers features such as a full-text search engine, vector database, LLM interface, and various utilities. The tool is currently under active development and not recommended for production use due to potential API changes. OramaCore aims to provide a comprehensive solution for managing data and enabling advanced AI capabilities in projects.

Airo is a tool designed to simplify the process of deploying containers to self-hosted servers. It allows users to focus on building their products without the complexity of Kubernetes or CI/CD pipelines. With Airo, users can easily build and push Docker images, deploy instantly with a single command, update configurations securely using SSH, and set up HTTPS and reverse proxy automatically using Caddy.

AgentIQ is a flexible library designed to seamlessly integrate enterprise agents with various data sources and tools. It enables true composability by treating agents, tools, and workflows as simple function calls. With features like framework agnosticism, reusability, rapid development, profiling, observability, evaluation system, user interface, and MCP compatibility, AgentIQ empowers developers to move quickly, experiment freely, and ensure reliability across agent-driven projects.

LangStream is a tool for natural language processing tasks, providing a CLI for easy installation and usage. Users can try sample applications like Chat Completions and create their own applications using the developer documentation. It supports running on Kubernetes for production-ready deployment, with support for various Kubernetes distributions and external components like Apache Kafka or Apache Pulsar cluster. Users can deploy LangStream locally using minikube and manage the cluster with mini-langstream. Development requirements include Docker, Java 17, Git, Python 3.11+, and PIP, with the option to test local code changes using mini-langstream.

The Mattermost AI Copilot Plugin is an extension that adds functionality for local and third-party LLMs within Mattermost v9.6 and above. It is currently experimental and allows users to interact with AI models seamlessly. The plugin enhances the user experience by providing AI-powered assistance and features for communication and collaboration within the Mattermost platform.

MinIO is a High Performance Object Storage released under GNU Affero General Public License v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads.

Manifold is a powerful platform for workflow automation using AI models. It supports text generation, image generation, and retrieval-augmented generation, integrating seamlessly with popular AI endpoints. Additionally, Manifold provides robust semantic search capabilities using PGVector combined with the SEFII engine. It is under active development and not production-ready.

Chatflow is a tool that provides a chat interface for users to interact with systems using natural language. The engine understands user intent and executes commands for tasks, allowing easy navigation of complex websites/products. This approach enhances user experience, reduces training costs, and boosts productivity.

Markdown preview server for AI coding agents. mdserve is a tool that allows AI agents to write markdown and see it rendered live in the browser. It features zero configuration, single binary installation, instant live reload via WebSocket, ephemeral sessions, and agent-friendly content support. It is not a documentation site generator, static site server, or general-purpose markdown authoring tool. mdserve is designed for AI coding agents to produce content like tables, diagrams, and code blocks.

Batteries Included is an all-in-one platform for building and running modern applications, simplifying cloud infrastructure complexity. It offers production-ready capabilities through an intuitive interface, focusing on automation, security, and enterprise-grade features. The platform includes databases like PostgreSQL and Redis, AI/ML capabilities with Jupyter notebooks, web services deployment, security features like SSL/TLS management, and monitoring tools like Grafana dashboards. Batteries Included is designed to streamline infrastructure setup and management, allowing users to concentrate on application development without dealing with complex configurations.

The pgedge-postgres-mcp repository contains a set of tools and scripts for managing and monitoring PostgreSQL databases in an edge computing environment. It provides functionalities for automating database tasks, monitoring database performance, and ensuring data integrity in edge computing scenarios. The tools are designed to be lightweight and efficient, making them suitable for resource-constrained edge devices. With pgedge-postgres-mcp, users can easily deploy and manage PostgreSQL databases in edge computing environments with minimal overhead.

Pathway's LLM (Large Language Model) Apps provide a platform to quickly deploy AI applications using the latest knowledge from data sources. The Python application examples in this repository are Docker-ready, exposing an HTTP API to the frontend. These apps utilize the Pathway framework for data synchronization, API serving, and low-latency data processing without the need for additional infrastructure dependencies. They connect to document data sources like S3, Google Drive, and Sharepoint, offering features like real-time data syncing, easy alert setup, scalability, monitoring, security, and unification of application logic.

Kaytu is an AI platform that enhances cloud efficiency by analyzing historical usage data and providing intelligent recommendations for optimizing instance sizes. Users can pay for only what they need without compromising the performance of their applications. The platform is easy to use with a one-line command, allows customization for specific requirements, and ensures security by extracting metrics from the client side. Kaytu is open-source and supports AWS services, with plans to expand to GCP, Azure, GPU optimization, and observability data from Prometheus in the future.

This repository is a curated list of open-source libraries for production large language models. It includes tools for data preprocessing, training/finetuning, evaluation/benchmarking, serving/inference, application/RAG, testing/monitoring, and guardrails/security. The repository also provides a new category called LLM Cookbook/Examples for showcasing examples and guides on using various LLM APIs.

Holistic AI is an open-source library dedicated to assessing and improving the trustworthiness of AI systems. It focuses on measuring and mitigating bias, explainability, robustness, security, and efficacy in AI models. The tool provides comprehensive metrics, mitigation techniques, a user-friendly interface, and visualization tools to enhance AI system trustworthiness. It offers documentation, tutorials, and detailed installation instructions for easy integration into existing workflows.

LangKit is an open-source text metrics toolkit for monitoring language models. It offers methods for extracting signals from input/output text, compatible with whylogs. Features include text quality, relevance, security, sentiment, toxicity analysis. Installation via PyPI. Modules contain UDFs for whylogs. Benchmarks show throughput on AWS instances. FAQs available.

Nesa is a tool that allows users to run on-prem AI for a fraction of the cost through a blind API. It provides blind privacy, zero latency on protected inference, wide model coverage, cost savings compared to cloud and on-prem AI, RAG support, and ChatGPT compatibility. Nesa achieves blind AI through Equivariant Encryption (EE), a new security technology that provides complete inference encryption with no additional latency. EE allows users to perform inference on neural networks without exposing the underlying data, preserving data privacy and security.

AirGo is a front and rear end separation, multi user, multi protocol proxy service management system, simple and easy to use. It supports vless, vmess, shadowsocks, and hysteria2.

Mosec is a high-performance and flexible model serving framework for building ML model-enabled backend and microservices. It bridges the gap between any machine learning models you just trained and the efficient online service API. * **Highly performant** : web layer and task coordination built with Rust 🦀, which offers blazing speed in addition to efficient CPU utilization powered by async I/O * **Ease of use** : user interface purely in Python 🐍, by which users can serve their models in an ML framework-agnostic manner using the same code as they do for offline testing * **Dynamic batching** : aggregate requests from different users for batched inference and distribute results back * **Pipelined stages** : spawn multiple processes for pipelined stages to handle CPU/GPU/IO mixed workloads * **Cloud friendly** : designed to run in the cloud, with the model warmup, graceful shutdown, and Prometheus monitoring metrics, easily managed by Kubernetes or any container orchestration systems * **Do one thing well** : focus on the online serving part, users can pay attention to the model optimization and business logic

The 'llm-code-interpreter' repository is a deprecated plugin that provides a code interpreter on steroids for ChatGPT by E2B. It gives ChatGPT access to a sandboxed cloud environment with capabilities like running any code, accessing Linux OS, installing programs, using filesystem, running processes, and accessing the internet. The plugin exposes commands to run shell commands, read files, and write files, enabling various possibilities such as running different languages, installing programs, starting servers, deploying websites, and more. It is powered by the E2B API and is designed for agents to freely experiment within a sandboxed environment.

Pezzo is a fully cloud-native and open-source LLMOps platform that allows users to observe and monitor AI operations, troubleshoot issues, save costs and latency, collaborate, manage prompts, and deliver AI changes instantly. It supports various clients for prompt management, observability, and caching. Users can run the full Pezzo stack locally using Docker Compose, with prerequisites including Node.js 18+, Docker, and a GraphQL Language Feature Support VSCode Extension. Contributions are welcome, and the source code is available under the Apache 2.0 License.

Learn Cloud Applied Generative AI Engineering (GenEng) is a course focusing on the application of generative AI technologies in various industries. The course covers topics such as the economic impact of generative AI, the role of developers in adopting and integrating generative AI technologies, and the future trends in generative AI. Students will learn about tools like OpenAI API, LangChain, and Pinecone, and how to build and deploy Large Language Models (LLMs) for different applications. The course also explores the convergence of generative AI with Web 3.0 and its potential implications for decentralized intelligence.

This repository contains shared codebase for two projects: gcloud-aio and gcloud-rest. gcloud-aio is built for Python 3's asyncio, while gcloud-rest is a threadsafe requests-based implementation. It provides clients for Google Cloud services like Auth, BigQuery, Datastore, KMS, PubSub, Storage, and Task Queue. Users can install the library using pip and refer to the documentation for usage details. Developers can contribute to the project by following the contribution guide.

Fluid is an open source Kubernetes-native Distributed Dataset Orchestrator and Accelerator for data-intensive applications, such as big data and AI applications. It implements dataset abstraction, scalable cache runtime, automated data operations, elasticity and scheduling, and is runtime platform agnostic. Key concepts include Dataset and Runtime. Prerequisites include Kubernetes version > 1.16, Golang 1.18+, and Helm 3. The tool offers features like accelerating remote file accessing, machine learning, accelerating PVC, preloading dataset, and on-the-fly dataset cache scaling. Contributions are welcomed, and the project is under the Apache 2.0 license with a vendor-neutral approach.

AIGES is a core component of the Athena Serving Framework, designed as a universal encapsulation tool for AI developers to deploy AI algorithm models and engines quickly. By integrating AIGES, you can deploy AI algorithm models and engines rapidly and host them on the Athena Serving Framework, utilizing supporting auxiliary systems for networking, distribution strategies, data processing, etc. The Athena Serving Framework aims to accelerate the cloud service of AI algorithm models and engines, providing multiple guarantees for cloud service stability through cloud-native architecture. You can efficiently and securely deploy, upgrade, scale, operate, and monitor models and engines without focusing on underlying infrastructure and service-related development, governance, and operations.